Universität zu Köln

Lohmann, Philipp ORCID: 0000-0002-5360-046X, Galldiks, Norbert, Kocher, Martin, Heinzel, Alexander, Filss, Christian P., Stegmayr, Carina, Mottaghy, Felix M., Fink, Gereon R. ORCID: 0000-0002-8230-1856, Shah, N. Jon ORCID: 0000-0002-8151-6169 and Langen, Karl-Josef (2021). Radiomics in neuro-oncology: Basics, workflow, and applications. Methods, 188. S. 112 - 122. SAN DIEGO: ACADEMIC PRESS INC ELSEVIER SCIENCE. ISSN 1095-9130

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Abstract

Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and cost-effective evaluation of imaging data is hardly feasible without the support of methods from the field of artificial intelligence (AI). AI can facilitate and shorten various timeconsuming steps in the image processing workflow, e.g., tumor segmentation, thereby optimizing productivity. Besides, the automated and computer-based analysis of imaging data may help to increase data comparability as it is independent of the experience level of the evaluating clinician. Importantly, AI offers the potential to extract new features from the routinely acquired neuroimages of brain tumor patients. In combination with patient data such as survival, molecular markers, or genomics, mathematical models can be generated that allow, for example, the prediction of treatment response or prognosis, as well as the noninvasive assessment of molecular markers. The subdiscipline of AI dealing with the computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is termed radiomics. This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Lohmann, Philipp UNSPECIFIED orcid.org/0000-0002-5360-046X UNSPECIFIED Galldiks, Norbert UNSPECIFIED UNSPECIFIED UNSPECIFIED Kocher, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED Heinzel, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Filss, Christian P. UNSPECIFIED UNSPECIFIED UNSPECIFIED Stegmayr, Carina UNSPECIFIED UNSPECIFIED UNSPECIFIED Mottaghy, Felix M. UNSPECIFIED UNSPECIFIED UNSPECIFIED Fink, Gereon R. UNSPECIFIED orcid.org/0000-0002-8230-1856 UNSPECIFIED Shah, N. Jon UNSPECIFIED orcid.org/0000-0002-8151-6169 UNSPECIFIED Langen, Karl-Josef UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-598572
DOI:	10.1016/j.ymeth.2020.06.003
Journal or Publication Title:	Methods
Volume:	188
Page Range:	S. 112 - 122
Date:	2021
Publisher:	ACADEMIC PRESS INC ELSEVIER SCIENCE
Place of Publication:	SAN DIEGO
ISSN:	1095-9130
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Biochemical Research Methods; Biochemistry & Molecular Biology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/59857